Abstract
Abstract. In this synthesis paper addressing hydrologic scaling and similarity, we posit that roadblocks in the search for universal laws of hydrology are hindered by our focus on computational simulation (the third paradigm) and assert that it is time for hydrology to embrace a fourth paradigm of data-intensive science. Advances in information-based hydrologic science, coupled with an explosion of hydrologic data and advances in parameter estimation and modeling, have laid the foundation for a data-driven framework for scrutinizing hydrological scaling and similarity hypotheses. We summarize important scaling and similarity concepts (hypotheses) that require testing; describe a mutual information framework for testing these hypotheses; describe boundary condition, state, flux, and parameter data requirements across scales to support testing these hypotheses; and discuss some challenges to overcome while pursuing the fourth hydrological paradigm. We call upon the hydrologic sciences community to develop a focused effort towards adopting the fourth paradigm and apply this to outstanding challenges in scaling and similarity.
Highlights
This synthesis paper is an outcome of the “Symposium in Honor of Eric Wood: Observations and Modeling across Scales”, held 2–3 June 2016 in Princeton, New Jersey, USA
In this synthesis paper addressing hydrologic scaling and similarity, we posit that roadblocks in the search for universal laws of hydrology are hindered by our focus on computational simulation and assert that it is time for hydrology to embrace a fourth paradigm of dataintensive science
We argue here that the growth of hydrologic science, from empiricism, via theory, to computational simulation has yielded important advances in understanding and predictive capabilities – yet we argue that accelerating advances in hydrologic science will require us to embrace the fourth paradigm of data-intensive science, to use emerging datasets to synthesize and scrutinize theories and models, and improve the data support for the mechanisms of Earth system change
Summary
This synthesis paper is an outcome of the “Symposium in Honor of Eric Wood: Observations and Modeling across Scales”, held 2–3 June 2016 in Princeton, New Jersey, USA. With the advent of high-resolution Earth observing systems (McCabe et al, 2017), proximal sensing (Robinson et al, 2008), sensor networks (Xia et al, 2015), and advances in data-intensive hydrologic science (e.g., Nearing and Gupta, 2015), there is an opportunity to recast the hydrologic scaling problem into a data-driven hypothesis testing framework (e.g., Rakovec et al, 2016a). By embracing such a framework, hydrologic analysis can become explicitly “scale-aware” by testing specific parameterizations on a given model scale. With this goal in mind, this paper addresses the following questions: 1. What are the key scaling and similarity concepts (hypotheses) that require testing?
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